SUBJECTS : Crop Sciences | Plant Phenotyping | Plant Breeding | Agriculture | Plant Biology | Plant Scienc
Plant Phenomics: 2023, vol: 5, issue:
- 1)- Cheng Zhang, Jingjing Kong, Daosheng Wu, Zhiyong Guan, Baoqing Ding, Fadi Chen. Wearable Sensor: An Emerging Data Collection Tool for Plant Phenotyping. Plant phenomics (Washington, D.C.). 2023, 5: 0051
Cited : 12Read More - 2)- Jonas Anderegg, Radek Zenkl, Achim Walter, Andreas Hund, Bruce A McDonald. Combining High-Resolution Imaging, Deep Learning, and Dynamic Modeling to Separate Disease and Senescence in Wheat Canopies. Plant phenomics (Washington, D.C.). 2023, 5: 0053Cited : 11Read More
- 3)- Sally Shuxian Koh, Kapil Dev, Javier Jingheng Tan, Valerie Xinhui Teo, Shuyan Zhang, Dinish U S, Malini Olivo, Daisuke Urano. Classification of Plant Endogenous States Using Machine Learning-Derived Agricultural Indices. Plant phenomics (Washington, D.C.). 2023, 5: 0060Cited : 1Read More
- 4)- Qianding Huang, Xingcai Wu, Qi Wang, Xinyu Dong, Yongbin Qin, Xue Wu, Yangyang Gao, Gefei Hao. Knowledge Distillation Facilitates the Lightweight and Efficient Plant Diseases Detection Model. Plant phenomics (Washington, D.C.). 2023, 5: 0062Cited : 8Read More
- 5)- Taeko Koji, Hiroyoshi Iwata, Motoyuki Ishimori, Hideki Takanashi, Yuji Yamasaki, Hisashi Tsujimoto. Multispectral Phenotyping and Genetic Analyses of Spring Appearance in Greening Plant, spp. Plant phenomics (Washington, D.C.). 2023, 5: 0063Cited : 1Read More
- 6)- Yangmingrui Gao, Yinglun Li, Ruibo Jiang, Xiaohai Zhan, Hao Lu, Wei Guo, Wanneng Yang, Yanfeng Ding, Shouyang Liu. Enhancing Green Fraction Estimation in Rice and Wheat Crops: A Self-Supervised Deep Learning Semantic Segmentation Approach. Plant phenomics (Washington, D.C.). 2023, 5: 0064Cited : 8Read More
- 7)- Qiushi Yu, Jingqi Wang, Hui Tang, Jiaxi Zhang, Wenjie Zhang, Liantao Liu, Nan Wang. Application of Improved UNet and EnglightenGAN for Segmentation and Reconstruction of In Situ Roots. Plant phenomics (Washington, D.C.). 2023, 5: 0066Cited : 2Read More
- 8)- Wenli Zhang, Yuxin Liu, Chao Zheng, Guoqiang Cui, Wei Guo. EasyDAM_V3: Automatic Fruit Labeling Based on Optimal Source Domain Selection and Data Synthesis via a Knowledge Graph. Plant phenomics (Washington, D.C.). 2023, 5: 0067Cited : 1Read More
- 9)- Dominik Rößle, Lukas Prey, Ludwig Ramgraber, Anja Hanemann, Daniel Cremers, Patrick Ole Noack, Torsten Schön. Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset. Plant phenomics (Washington, D.C.). 2023, 5: 0068Cited : 5Read More
- 10)- Ruina Zhao, Yujie Guan, Yuqi Lu, Ze Ji, Xiang Yin, Weikuan Jia. FCOS-LSC: A Novel Model for Green Fruit Detection in a Complex Orchard Environment. Plant phenomics (Washington, D.C.). 2023, 5: 0069Cited : 4Read More
- 11)- Stefan Paulus, Benjamin Leiding. Can Distributed Ledgers Help to Overcome the Need of Labeled Data for Agricultural Machine Learning Tasks? Plant phenomics (Washington, D.C.). 2023, 5: 0070Cited : 3Read More
- 12)- Jinnuo Zhang, Xuping Feng, Jian Jin, Hui Fang. Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping. Plant phenomics (Washington, D.C.). 2023, 5: 0071Cited : 1Read More
- 13)- John Lagergren, Mirko Pavicic, Hari B Chhetri, Larry M York, Doug Hyatt, David Kainer, Erica M Rutter, Kevin Flores, Jack Bailey-Bale, Marie Klein, Gail Taylor, Daniel Jacobson, Jared Streich. Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in . Plant phenomics (Washington, D.C.). 2023, 5: 0072Cited : 4Read More
- 14)- Ting Luo, Xiaoyan Liu, Prakash Lakshmanan. A Combined Genomics and Phenomics Approach is Needed to Boost Breeding in Sugarcane. Plant phenomics (Washington, D.C.). 2023, 5: 0074Cited : 3Read More
- 15)- Qingfeng Song, Fusang Liu, Hongyi Bu, Xin-Guang Zhu. Quantifying Contributions of Different Factors to Canopy Photosynthesis in 2 Maize Varieties: Development of a Novel 3D Canopy Modeling Pipeline. Plant phenomics (Washington, D.C.). 2023, 5: 0075Cited : 6Read More
- 16)- Tobias Selzner, Jannis Horn, Magdalena Landl, Andreas Pohlmeier, Dirk Helmrich, Katrin Huber, Jan Vanderborght, Harry Vereecken, Sven Behnke, Andrea Schnepf. 3D U-Net Segmentation Improves Root System Reconstruction from 3D MRI Images in Automated and Manual Virtual Reality Work Flows. Plant phenomics (Washington, D.C.). 2023, 5: 0076Cited : 2Read More
- 17)- Houssem E M Triki, Fabienne Ribeyre, Fabrice Pinard, Marc Jaeger. Coupling Plant Growth Models and Pest and Disease Models: An Interaction Structure Proposal, MIMIC. Plant phenomics (Washington, D.C.). 2023, 5: 0077Cited : 2Read More
- 18)- Bo Yao, Xiaolong Wang, Yancheng Wang, Tianyang Ye, Enli Wang, Qiang Cao, Xia Yao, Yan Zhu, Weixing Cao, Xiaojun Liu, Liang Tang. Interaction of Genotype, Environment, and Management on Organ-Specific Critical Nitrogen Dilution Curve in Wheat. Plant phenomics (Washington, D.C.). 2023, 5: 0078Cited : 0Read More
- 19)- Liyi Luo, Xintong Jiang, Yu Yang, Eugene Roy Antony Samy, Mark Lefsrud, Valerio Hoyos-Villegas, Shangpeng Sun. Eff-3DPSeg: 3D Organ-Level Plant Shoot Segmentation Using Annotation-Efficient Deep Learning. Plant phenomics (Washington, D.C.). 2023, 5: 0080Cited : 9Read More
- 20)- Shunfu Xiao, Shuaipeng Fei, Qing Li, Bingyu Zhang, Haochong Chen, Demin Xu, Zhibo Cai, Kaiyi Bi, Yan Guo, Baoguo Li, Zhen Chen, Yuntao Ma. The Importance of Using Realistic 3D Canopy Models to Calculate Light Interception in the Field. Plant phenomics (Washington, D.C.). 2023, 5: 0082Cited : 4Read More
- 21)- Alexis Carlier, Sebastien Dandrifosse, Benjamin Dumont, Benoît Mercatoris. To What Extent Does Yellow Rust Infestation Affect Remotely Sensed Nitrogen Status? Plant phenomics (Washington, D.C.). 2023, 5: 0083Cited : 0Read More
- 22)- Amogh Joshi, Dario Guevara, Mason Earles. Standardizing and Centralizing Datasets for Efficient Training of Agricultural Deep Learning Models. Plant phenomics (Washington, D.C.). 2023, 5: 0084Cited : 6Read More
- 23)- Wensheng Du, Ping Liu. Instance Segmentation and Berry Counting of Table Grape before Thinning Based on AS-SwinT. Plant phenomics (Washington, D.C.). 2023, 5: 0085Cited : 4Read More
- 24)- Haozhou Wang, Tang Li, Erika Nishida, Yoichiro Kato, Yuya Fukano, Wei Guo. Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income. Plant phenomics (Washington, D.C.). 2023, 5: 0086Cited : 1Read More
- 25)- Yapeng Wu, Wenhui Wang, Yangyang Gu, Hengbiao Zheng, Xia Yao, Yan Zhu, Weixing Cao, Tao Cheng. SPSI: A Novel Composite Index for Estimating Panicle Number in Winter Wheat before Heading from UAV Multispectral Imagery. Plant phenomics (Washington, D.C.). 2023, 5: 0087Cited : 0Read More
- 26)- Jiangchuan Bao, Guo Li, Haolan Mo, Tingting Qian, Ming Chen, Shenglian Lu. Detection and Reconstruction of Passion Fruit Branches via CNN and Bidirectional Sector Search. Plant phenomics (Washington, D.C.). 2023, 5: 0088Cited : 2Read More
- 27)- Pengpeng Zhang, Jingyao Huang, Yuntao Ma, Xiujuan Wang, Mengzhen Kang, Youhong Song. Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery. Plant phenomics (Washington, D.C.). 2023, 5: 0091Cited : 2Read More
- 28)- Wen Gao, Xiaoming Yang, Lin Cao, Fuliang Cao, Hao Liu, Quan Qiu, Meng Shen, Pengfei Yu, Yuhua Liu, Xin Shen. Screening of Ginkgo Individuals with Superior Growth Structural Characteristics in Different Genetic Groups Using Terrestrial Laser Scanning (TLS) Data. Plant phenomics (Washington, D.C.). 2023, 5: 0092Cited : 0Read More
- 29)- Min Li, Pengcheng Hu, Di He, Bangyou Zheng, Yan Guo, Yushan Wu, Tao Duan. Quantification of the Cumulative Shading Capacity in a Maize-Soybean Intercropping System Using an Unmanned Aerial Vehicle. Plant phenomics (Washington, D.C.). 2023, 5: 0095Cited : 0Read More
- 30)- Rahul Chandnani, Tongfei Qin, Heng Ye, Haifei Hu, Karim Panjvani, Mutsutomo Tokizawa, Javier Mora Macias, Alma Armenta Medina, Karine Bernardino, Pierre-Luc Pradier, Pankaj Banik, Ashlyn Mooney, Jurandir V Magalhaes, Henry T Nguyen, Leon V Kochian. Application of an Improved 2-Dimensional High-Throughput Soybean Root Phenotyping Platform to Identify Novel Genetic Variants Regulating Root Architecture Traits. Plant phenomics (Washington, D.C.). 2023, 5: 0097Cited : 2Read More
- 31)- Junfeng Chen, Yun Wang, Peng Di, Yulong Wu, Shi Qiu, Zongyou Lv, Yuqi Qiao, Yajing Li, Jingfu Tan, Weixu Chen, Ma Yu, Ping Wei, Ying Xiao, Wansheng Chen. Phenotyping of Roots Reveals Associations between Root Traits and Bioactive Components. Plant phenomics (Washington, D.C.). 2023, 5: 0098Cited : 2Read More
- 32)- Yi Yu, Qin Cheng, Fei Wang, Yulei Zhu, Xiaoguang Shang, Ashley Jones, Haohua He, Youhong Song. Crop/Plant Modeling Supports Plant Breeding: I. Optimization of Environmental Factors in Accelerating Crop Growth and Development for Speed Breeding. Plant phenomics (Washington, D.C.). 2023, 5: 0099Cited : 2Read More
- 33)- Haoyu Zheng, Xijian Fan, Weihao Bo, Xubing Yang, Tardi Tjahjadi, Shichao Jin. A Multiscale Point-Supervised Network for Counting Maize Tassels in the Wild. Plant phenomics (Washington, D.C.). 2023, 5: 0100Cited : 26Read More
- 34)- Zhuo Liu, Mahmoud Al-Sarayreh, Cong Xu, Federico Tomasetto, Yanjie Li. : An R Package and Tool for Extracting Tree Spectra from UAV-Based Remote Sensing. Plant phenomics (Washington, D.C.). 2023, 5: 0103Cited : 0Read More
- 35)- Flavian Tschurr, Norbert Kirchgessner, Andreas Hund, Lukas Kronenberg, Jonas Anderegg, Achim Walter, Lukas Roth. Frost Damage Index: The Antipode of Growing Degree Days. Plant phenomics (Washington, D.C.). 2023, 5: 0104Cited : 4Read More
- 36)- Zixuan Teng, Jiawei Chen, Jian Wang, Shuixiu Wu, Riqing Chen, Yaohai Lin, Liyan Shen, Robert Jackson, Ji Zhou, Changcai Yang. Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice. Plant phenomics (Washington, D.C.). 2023, 5: 0105Cited : 5Read More
- 37)- Fan Zhang, Bo Wang, Fuhao Lu, Xinhong Zhang. Rotating Stomata Measurement Based on Anchor-Free Object Detection and Stomata Conductance Calculation. Plant phenomics (Washington, D.C.). 2023, 5: 0106Cited : 1Read More
- 38)- Jianqing Zhao, Yucheng Cai, Suwan Wang, Jiawei Yan, Xiaolei Qiu, Xia Yao, Yongchao Tian, Yan Zhu, Weixing Cao, Xiaohu Zhang. Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet. Plant phenomics (Washington, D.C.). 2023, 5: 0109Cited : 5Read More
- 39)- Jan Stejskal, Jaroslav Čepl, Eva Neuwirthová, Olusegun Olaitan Akinyemi, Jiří Chuchlík, Daniel Provazník, Markku Keinänen, Petya Campbell, Jana Albrechtová, Milan Lstibůrek, Zuzana Lhotáková. Making the Genotypic Variation Visible: Hyperspectral Phenotyping in Scots Pine Seedlings. Plant phenomics (Washington, D.C.). 2023, 5: 0111Cited : 2Read More
- 40)- Shamprikta Mehreen, Hervé Goëau, Pierre Bonnet, Sophie Chau, Julien Champ, Alexis Joly. Estimating Compositions and Nutritional Values of Seed Mixes Based on Vision Transformers. Plant phenomics (Washington, D.C.). 2023, 5: 0112Cited : 0Read More
- 41)- Yanan Li, Yuling Tang, Yifei Liu, Dingrun Zheng. Semi-supervised Counting of Grape Berries in the Field Based on Density Mutual Exclusion. Plant phenomics (Washington, D.C.). 2023, 5: 0115Cited : 3Read More
- 42)- Elsa Chedid, Komlan Avia, Vincent Dumas, Lionel Ley, Nicolas Reibel, Gisèle Butterlin, Maxime Soma, Raul Lopez-Lozano, Frédéric Baret, Didier Merdinoglu, Éric Duchêne. LiDAR Is Effective in Characterizing Vine Growth and Detecting Associated Genetic Loci. Plant phenomics (Washington, D.C.). 2023, 5: 0116Cited : 1Read More
- 43)- Tingting Wu, Peng Shen, Jianlong Dai, Yuntao Ma, Yi Feng. A Pathway to Assess Genetic Variation of Wheat Germplasm by Multidimensional Traits with Digital Images. Plant phenomics (Washington, D.C.). 2023, 5: 0119Cited : 0Read More
- 44)- Laëtitia Lemiere, Marc Jaeger, Marie Gosme, Gérard Subsol. Combinatorial Maps, a New Framework to Model Agroforestry Systems. Plant phenomics (Washington, D.C.). 2023, 5: 0120Cited : 1Read More
- 45)- Ruowen Liu, Pengyan Li, Zejun Li, Zhenghui Liu, Yanfeng Ding, Wenjuan Li, Shouyang Liu. Bio-Master: Design and Validation of a High-Throughput Biochemical Profiling Platform for Crop Canopies. Plant phenomics (Washington, D.C.). 2023, 5: 0121Cited : 0Read More
- 46)- Panli Zhang, Xiaobo Sun, Donghui Zhang, Yuechao Yang, Zhenhua Wang. Lightweight Deep Learning Models for High-Precision Rice Seedling Segmentation from UAV-Based Multispectral Images. Plant phenomics (Washington, D.C.). 2023, 5: 0123Cited : 5Read More
- 47)- Jiayi Li, Haiyan Zeng, Chenxin Huang, Libin Wu, Jie Ma, Beibei Zhou, Dapeng Ye, Haiyong Weng. Noninvasive Detection of Salt Stress in Cotton Seedlings by Combining Multicolor Fluorescence-Multispectral Reflectance Imaging with EfficientNet-OB2. Plant phenomics (Washington, D.C.). 2023, 5: 0125Cited : 1Read More
- 48)- Sriram Parasurama, Darshi Banan, Kyungdahm Yun, Sharon Doty, Soo-Hyung Kim. Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture. Plant phenomics (Washington, D.C.). 2023, 5: 0127Cited : 0Read More
- 49)- Guohui Ding, Liyan Shen, Jie Dai, Robert Jackson, Shuchen Liu, Mujahid Ali, Li Sun, Mingxing Wen, Jin Xiao, Greg Deakin, Dong Jiang, Xiu-E Wang, Ji Zhou. The Dissection of Nitrogen Response Traits Using Drone Phenotyping and Dynamic Phenotypic Analysis to Explore N Responsiveness and Associated Genetic Loci in Wheat. Plant phenomics (Washington, D.C.). 2023, 5: 0128Cited : 4Read More
- 50)- Xinquan Ye, Jie Pan, Gaosheng Liu, Fan Shao. Exploring the Close-Range Detection of UAV-Based Images on Pine Wilt Disease by an Improved Deep Learning Method. Plant phenomics (Washington, D.C.). 2023, 5: 0129Cited : 4Read More
